Beating the Tit for Tat: Using a Genetic Algorithm to Build an Effective Adaptive Behavior

Agents capable of adaptive behavior can be obtained by means of AI tools. Thanks to these, they develop the ability to vary their Behavior in order to achieve satisfying results in the simulated environment. In the paper, artificially intelligent agents play an iterated prisoner' s dilemma against agents that reproduce (in a fix way) strategies that have emerged in Axelrod' s toumament. The objective of the adaptive agent is to earn a payoff higher than one of the Tit-for-tat, the strategy which has shown the better performance in the Axelrod's experimental setup. In the work, Genetic Algorithms are employed to produce and modify rules that are apt to achieve the set task. The adaptive dynamics is analysed in depth in order to understand the issues related to the codification of knowledge and to the evaluation of diverse strategies. In order to highlight different nuances of these matters we have amended the method as to improve it and experimented different knowledge's codifications.